High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

高级别儿科脑肿瘤在儿童中表现出最高的癌症死亡率。虽然常规 MRI 已被广泛用于临床检查小儿高级别脑肿瘤，但准确的神经影像学检测和肿瘤组织病理学的区分以改进诊断、手术计划和治疗评估，仍然是其临床管理中未满足的需求。我们采用了一种新颖的扩散组织学成像 (DHI) 方法，该方法采用扩散基础谱成像 (DBSI) 派生指标作为深度神经网络分析的输入分类器。DHI 旨在检测、区分和量化儿科高级别脑肿瘤的异质区域，包括正常白质 (WM)、密集细胞肿瘤、低密度细胞肿瘤、浸润边缘、坏死和出血。因此，不同的扩散度量组合将指示每个不同的肿瘤组织学特征的独特分布。DHI 通过结合 DBSI 指标和深度神经网络算法，对小儿肿瘤组织学进行分类，总体准确率为 85.8%。接受者操作分析 (ROC) 分析表明，DHI 在区分个体肿瘤组织学方面具有强大的能力，其 AUC 值 (95% CI) 为 0.984 (0.982–0.986)、0.960 (0.956–0.963)、0.991 (0.990–0.995)、(004–004–004) 0.956)、0.977 (0.973–0.981) 和 0.976 (0.972–0.979) 分别用于正常 WM、密集细胞肿瘤、细胞密度较低的肿瘤、浸润边缘、坏死和出血。我们的结果表明，DBSI-DNN 或 DHI 可以准确表征和分类儿科高级别脑肿瘤的多种肿瘤组织学特征。